Analysis of active semi-supervised learning

Author:

Berton Lilian1ORCID,Mitsuishi Felipe1ORCID,Vega Oliveros Didier2ORCID

Affiliation:

1. Science and Technology, Universidade Federal de São Paulo, São José dos Campos, São Paulo, Brazil

2. Instituto de Computação, Universidade de Campinas, Campinas, São Paulo, Brazil

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

ACM

Reference24 articles.

1. L. Berton , A. de Andrade Lopes, and D. A. Vega-Oliveros. 2018. A Comparison of Graph Construction Methods for Semi-Supervised Learning . In 2018 International Joint Conference on Neural Networks (IJCNN). 1--8. L. Berton, A. de Andrade Lopes, and D. A. Vega-Oliveros. 2018. A Comparison of Graph Construction Methods for Semi-Supervised Learning. In 2018 International Joint Conference on Neural Networks (IJCNN). 1--8.

2. RGCLI: Robust Graph that Considers Labeled Instances for Semi-Supervised Learning;Berton Lilian;Neurocomputing,2017

3. Lilian Berton , Didier Augusto Vega-Oliveros , Jorge Carlos Valverde-Rebaza , Andre Tavares da Silva , and Alneu de Andrade Lopes . 2016 . The Impact of Network Sampling on Relational Classification. In 3rd Annual International Symposium on Information Management and Big Data - SIMBig. 62--72 . Lilian Berton, Didier Augusto Vega-Oliveros, Jorge Carlos Valverde-Rebaza, Andre Tavares da Silva, and Alneu de Andrade Lopes. 2016. The Impact of Network Sampling on Relational Classification. In 3rd Annual International Symposium on Information Management and Big Data - SIMBig. 62--72.

4. Active learning for semi-supervised structural health monitoring;Bull L.;Journal of Sound and Vibration,2018

5. Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data;Calma Adrian;Information Sciences,2018

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